DETAILED ACTION
Status of Claims
This Office action is responsive to communications filed on 2024-06-25. Claim(s) 1-20 is/are pending and are examined herein.
Claim(s) 1-20 is/are objected to.
Claim(s) 1-20 is/are rejected under 35 USC 112(b).
Claim(s) 1-20 is/are rejected under 35 USC 101.
Claim(s) 1-20 is/are rejected under 35 USC 102.
Notice of Pre-AIA or AIA Status
The present application, filed on or after 2013-03-16, is being examined under the first inventor to file provisions of the AIA .
Priority
The present application claims priority from US Provisional Application 63/653,914, filed 2024-05-30. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The attached information disclosure statement(s) (IDS), submitted on 2024-07-12, 2025-03-06, 2025-10-07, 2025-11-03, 2025-11-18, 2025-11-25, 2026-01-05, 2026-03-19, and 2026-04-30 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the attached information disclosure statement(s) is/are being considered by the examiner.
Claim Objections
Claim(s) 1-20 is/are objected to because of the following informalities:
Claims 1, 9, and 17 recite using the at least one second layer of the multi-layer memory [emphasis added] but the underlined phrase lacks antecedent basis. It should be “using the
Claims 1, 9, and 17 recite including the at least one machine-learned preference [emphasis added] but the underlined phrase lacks antecedent basis. It should be “including the at least one machine-learned entity preference” for proper antecedent basis. The applicant is invited to consult a related 112(b) rejection regarding this limitation.
Appropriate correction is required.
Claim Rejections - 35 USC 112(b)
The following is a quotation of 35 USC 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 USC 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1-20 is/are rejected under 35 USC 112(b) or 35 USC 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 USC 112, the applicant), regards as the invention.
Claims 1, 9, and 17 recite store/ing at least one machine-learned entity preference in a second layer of the multi-layer memory, wherein the at least one machine-learned entity preference is machine-learned using the context data [emphasis added] but the meaning of these limitations is not clear. A model can be “machine-learned” (i.e., trained), but it is not clear what it means for a preference to be “machine-learned” as recited in this limitation. Moreover, the specification provides no special definition for the verb “machine-learn” that clarifies what it means for a preference (as opposed to a model) to be “machine-learned” as recited by this limitation. MPEP 2173.05(b)(II) indicates that a “claim may be rendered indefinite when a limitation of the claim is defined by reference to an object and the relationship between the limitation and the object is not sufficiently defined. That is, where the elements of a claim have two or more plausible constructions such that the examiner cannot readily ascertain positional relationship of the elements, the claim may be rendered indefinite”. In the present instance, the relationship between machine learning and the “machine-learned entity preference” is insufficiently defined and the claim is consequently indefinite. For the purpose of compact prosecution, the limitation is interpreted broadly as encompassing any preference which makes use of a machine learning model in some way. Alternative language clarifying the relationship between the entity preference and the machine learning is required. Dependent claims 2-8, 10-16, and 18-20 inherit the rejection. Moreover, the dependent claims include similar issues of indefiniteness that require resolution. For example, claims 2, 10, and 18 recite a machine-learned difference between the data obtained via the interaction and a machine-generated probable interaction between the entity and the automated agent [emphasis added] but it is not clear what it means for a difference to be “machine-learned”. Similarly, claims 7 and 15 recite machine-learning a definition or example of a term [emphasis added] but, again, it is not clear what it means to “machine-learn” a definition or example. Similarly, claims 8 and 16 recite machine-learning a group or series of steps of the workflow; and storing the machine-learned group or series of steps of the workflow [emphasis added] but again, it is not clear what it means to “machine-learn” a group or series of steps.
Claims 5 and 13 recites using a machine learning model [emphasis added] but their respective parent claims already recite “a machine learning model”. The repeat nomenclature renders unclear whether the “machine learning model” of the dependent is the same machine learning model as the one recited in the parent claim (in which, the dependent should read “using the machine learning model”) or some other model (in which case, distinct nomenclature is required). For the purpose of compact prosecution, the claim is interpreted broadly as encompassing either of these interpretations.
Claim Rejections - 35 USC 101
35 USC 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 is/are rejected under 35 USC 101 because the claimed invention(s) is/are directed to abstract ideas without significantly more.
Claim 1
Step 1. The claim and its dependents 2-8 fall(s) under the statutory category of methods. An analysis of step 2 for each of these claims follows.
Step 2A Prong 1. The claim recites the following abstract ideas:
A method comprising: determining an entity identity associated with an entity; (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
and using the at least one second layer of the multi-layer memory including the at least one machine-learned preference to configure or control execution of the workflow (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
Step 2A Prong 2. The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
using the entity identity, creating an automated agent comprising a multi-layer memory and a workflow; (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
storing context data in a first layer of the multi-layer memory, (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
wherein the context data is obtained using the entity identity; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
storing at least one machine-learned entity preference in a second layer of the multi-layer memory, (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
wherein the at least one machine-learned entity preference is machine-learned using the context data; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
by the automated agent. (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B. The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
using the entity identity, creating an automated agent comprising a multi-layer memory and a workflow; (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
storing context data in a first layer of the multi-layer memory, (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
wherein the context data is obtained using the entity identity; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
storing at least one machine-learned entity preference in a second layer of the multi-layer memory, (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
wherein the at least one machine-learned entity preference is machine-learned using the context data; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
by the automated agent. (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Claim 2
Step 2A Prong 1. The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2. The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
[The method of claim 1, further comprising:] storing data via an interaction between the entity and the automated agent in the first layer of the multi-layer memory; (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
and storing a machine-learned difference between the data obtained via the interaction and a machine-generated probable interaction between the entity and the automated agent in the second layer of the multi-layer memory. (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
Step 2B. The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
[The method of claim 1, further comprising:] storing data via an interaction between the entity and the automated agent in the first layer of the multi-layer memory; (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
and storing a machine-learned difference between the data obtained via the interaction and a machine-generated probable interaction between the entity and the automated agent in the second layer of the multi-layer memory. (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
Claim 3
Step 2A Prong 1. The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
creating a compressed version of the data obtained via the interaction; (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
Step 2A Prong 2. The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
[The method of claim 1, further comprising:] storing data obtained via an interaction between the entity and the automated agent in the first layer of the multi-layer memory; (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
and storing the compressed version of the data obtained via the interaction in the second layer of the multi-layer memory. (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
Step 2B. The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
[The method of claim 1, further comprising:] storing data obtained via an interaction between the entity and the automated agent in the first layer of the multi-layer memory; (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
and storing the compressed version of the data obtained via the interaction in the second layer of the multi-layer memory. (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
Claim 4
Step 2A Prong 1. The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
[The method of claim 1, further comprising:] querying at least one of the first layer or the second layer for argument data; (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
mapping the argument data to at least one argument of a prompt; (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
generate a plan; (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
Step 2A Prong 2. The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
applying a machine learning model to the prompt including the at least one argument to (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
and causing the automated agent to execute the plan. (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B. The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
applying a machine learning model to the prompt including the at least one argument to (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
and causing the automated agent to execute the plan. (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Claim 5
Step 2A Prong 1. The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
determining an order of precedence for querying the first layer and the second layer; (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
and querying the first layer and the second layer in accordance with the order of precedence. (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
Step 2A Prong 2. The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
[The method of claim 4, further comprising:] using a machine learning model, (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B. The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
[The method of claim 4, further comprising:] using a machine learning model, (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Claim 6
Step 2A Prong 1. The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
[The method of claim 1, further comprising:] assigning a first access level to the first layer; (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
and assigning a second access level to the third layer, wherein the first access level is more restrictive than the second access level. (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
Step 2A Prong 2. The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
moving a subset of the context data to a third layer of the multi-layer memory; (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
Step 2B. The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
moving a subset of the context data to a third layer of the multi-layer memory; (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data transfer and/or electronic recordkeeping. See MPEP 2106.05(d)(II), “Receiving or transmitting data over a network” and/or “Storing and retrieving information in memory and/or “Electronic recordkeeping”.)
Claim 7
Step 2A Prong 1. The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2. The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
[The method of claim 1, further comprising:] using the first layer of the multi-layer memory, machine-learning a definition or example of a term; (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
and storing the machine-learned definition or example of the term in the second layer of the multi-layer memory. (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
Step 2B. The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
[The method of claim 1, further comprising:] using the first layer of the multi-layer memory, machine-learning a definition or example of a term; (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
and storing the machine-learned definition or example of the term in the second layer of the multi-layer memory. (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
Claim 8
Step 2A Prong 1. The claim recites the following abstract ideas:
The abstract idea(s) in the parent claim(s).
Step 2A Prong 2. The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
The additional element(s) in the parent claim(s).
[The method of claim 1, further comprising:] using the first layer of the multi-layer memory, machine-learning a group or series of steps of the workflow; (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
and storing the machine-learned group or series of steps of the workflow in the second layer of the multi-layer memory. (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
Step 2B. The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
The additional element(s) in the parent claim(s).
[The method of claim 1, further comprising:] using the first layer of the multi-layer memory, machine-learning a group or series of steps of the workflow; (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
and storing the machine-learned group or series of steps of the workflow in the second layer of the multi-layer memory. (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
Claim 9
Step 1. The claim and its dependents 10-16 fall(s) under the statutory category of machines. An analysis of step 2 for each of these claims follows.
Step 2A Prong 1. The claim recites the following abstract ideas:
determining an entity identity associated with an entity; (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
and using the at least one second layer of the multi-layer memory including the at least one machine-learned preference to configure or control execution of the workflow (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
Step 2A Prong 2. The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
A system comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises at least one instruction that, when executed by the at least one processor, causes the at least one processor to be capable of performing at least one operation comprising: (This generic computing components for performing an abstract idea. See MPEP 2106.05(f)(2).)
using the entity identity, creating an automated agent comprising a multi-layer memory and a workflow; (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
storing context data in a first layer of the multi-layer memory, (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
wherein the context data is obtained using the entity identity; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
storing at least one machine-learned entity preference in a second layer of the multi-layer memory, (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
wherein the at least one machine-learned entity preference is machine-learned using the context data; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
by the automated agent. (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B. The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
A system comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises at least one instruction that, when executed by the at least one processor, causes the at least one processor to be capable of performing at least one operation comprising: (This generic computing components for performing an abstract idea. See MPEP 2106.05(f)(2).)
using the entity identity, creating an automated agent comprising a multi-layer memory and a workflow; (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
storing context data in a first layer of the multi-layer memory, (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
wherein the context data is obtained using the entity identity; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
storing at least one machine-learned entity preference in a second layer of the multi-layer memory, (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
wherein the at least one machine-learned entity preference is machine-learned using the context data; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
by the automated agent. (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Claims 10-16 inherit limitations from claim 9 and recite additional limitations which are substantially similar to those recited by claims 2-8, respectively, so they are rejected by the same rationale.
Claim 17
Step 1. The claim and its dependents 18-20 fall under the statutory category of machines. An analysis of step 2 for each of these claims follows.
Step 2A Prong 1. The claim recites the following abstract ideas:
determine an entity identity associated with an entity; (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
and use the at least one second layer of the multi-layer memory including the at least one machine-learned preference to configure or control execution of the workflow (This recites a mental process that can be performed in the human mind or by a human using pen and paper. See MPEP 2106.04(a)(2)(III).)
Step 2A Prong 2. The claim recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application:
At least one non-transitory machine-readable storage medium comprising at least one instruction that, when executed by at least one processor, causes the at least one processor to: (This generic computing components for performing an abstract idea. See MPEP 2106.05(f)(2).)
using the entity identity, create an automated agent comprising a multi-layer memory and a workflow; (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
store context data in a first layer of the multi-layer memory, (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
wherein the context data is obtained using the entity identity; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
store at least one machine-learned entity preference in a second layer of the multi-layer memory, (This recites insignificant extra-solution activity. See MPEP 2106.05(g).)
wherein the at least one machine-learned entity preference is machine-learned using the context data; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
by the automated agent. (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Step 2B. The claim recites the following additional elements which, considered individually and as an ordered combination, do not amount to significantly more than the abstract idea:
At least one non-transitory machine-readable storage medium comprising at least one instruction that, when executed by at least one processor, causes the at least one processor to: (This generic computing components for performing an abstract idea. See MPEP 2106.05(f)(2).)
using the entity identity, create an automated agent comprising a multi-layer memory and a workflow; (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
store context data in a first layer of the multi-layer memory, (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
wherein the context data is obtained using the entity identity; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
store at least one machine-learned entity preference in a second layer of the multi-layer memory, (This insignificant extra-solution activity is well-understood, routine, conventional as it is mere data storage. See MPEP 2106.05(d)(II), “Electronic recordkeeping” and/or “Storing and retrieving information in memory”.)
wherein the at least one machine-learned entity preference is machine-learned using the context data; (This recites data of a particular type or source, merely linking an abstract idea to a particular field of use. See MPEP 2106.05(h).)
by the automated agent. (This recites merely applying (or equivalent) an abstract idea, or implementing an abstract idea on a computer, or using a computer as a tool to perform an abstract idea. See MPEP 2106.05(f).)
Claims 18-20 inherit limitations from claim 17 and recite additional limitations which are substantially similar to those recited by claims 2-4, respectively, so they are rejected by the same rationale.
Claim Rejections - 35 USC 102
The following is a quotation of the appropriate paragraphs of 35 USC 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 USC 102(a)(1) as being anticipated by Joon Sung PARK et al. (Generative Agents: Interactive Simulacra of Human Behavior, published 2023-08-06; hereafter, “Park”).
Claim 1
Park discloses:
A method comprising: determining an entity identity associated with an entity; using the entity identity, creating an automated agent comprising a multi-layer memory and a workflow; ([Park, sections 1, 3.1, and figure 5]: Park discusses “generative agents” based on large-language models that “create daily plans that reflect their characteristics and experiences, act out those plans, react, and re-plan when appropriate” [Park, section 1 paragraph beginning “generative agents”]. Each agent’s memory has two components: a “Memory Stream” and “Retrieved Memories” [Park, figure 5]. Park also discloses “author[ing] one paragraph of natural language description to depict each agent’s identity” and “[e]ach semicolon-delimited phrase [of the natural language description] is entered into the agent’s initial memory as memories at the start of the simulation” [Park, section 3.1 first paragraph]. Any of the agents maps to the “entity” of the claim. Its initial natural language description (or, alternatively, its memory stream, in which that natural language description is stored) maps to the “entity identity” of the claim. Any of the agents maps to the “automated agent” of the claim. Its multi-component memory structure maps to the “multi-layer memory” of the claim, and the agent’s plans and actions map to the “workflow” of the claim.)
storing context data in a first layer of the multi-layer memory, ([Park, figure 5]: As noted above, Park discloses each agent having two memory components: a memory stream and retrieved memories [Park, figure 5]. It discloses that “[a]gents perceive their environment, and all perceptions are saved in a comprehensive record of the agent’s experiences called the memory stream. Based on their perceptions, the architecture retrieves relevant memories” [Park, figure 5 caption]. The retrieved memories map to the “context data” and the “first layer” of the claim, and retrieving relevant memories maps to the step of “storing [the] context data in [the] first layer” as recited by the claim.)
wherein the context data is obtained using the entity identity; ([Park, section 3.1 and figure 5]: As noted above, the initial natural language description of each agent is “entered into the agent’s initial memory as memories at the start of the simulation” [Park, section 3.1 first paragraph]. Since the retrieved memories are retrieved from the memory stream, they are “obtained using the entity identity” as recited by the claim.)
storing at least one machine-learned entity preference in a second layer of the multi-layer memory, wherein the at least one machine-learned entity preference is machine-learned using the context data; ([Park, section 3 and figure 5]: As noted above, the initial natural language description of each agent is “entered into the agent’s initial memory as memories at the start of the simulation” [Park, section 3.1 first paragraph]. The initial natural language description includes the agent’s preferences (e.g., “John Lin… loves to help people. He is always looking for ways to make the process of getting medication easier for his customers… John Lin loves his family very much… John Lin thinks Sam Moore is a nice and kind man… John Lin and Tom Moreno are friends and like to discuss local politics together” [Park, section 3.1 first paragraph]). Moreover, as agent behavior is simulated, they “remember their interactions with other agents” [Park, section 3.4.2] and new preferences are added to the memory stream based on these actions (e.g., “I don’t like Sam Moore” [Park, section 3.1.1 last paragraph] or “We have all agreed to vote for him because we like his platform” [Park, section 3.1.2 first paragraph]). The memory stream maps to the “second layer” of the claim, and the agent’s preferences stored in the stream map to the “at least one machine-learned entity preference” of the claim. (The examiner notes tangentially that the claim as presently recited does not require the “first layer” and the “second layer” to be distinct from one another, so, in an alternative mapping of claim elements, the memory stream and the retrieved memories taken together could be mapped to the “first layer” of the claim as well as the “second layer” of the claim.) While the phrase “machine-learned” is indefinite as recited (cf. 112(b) rejections), at some of the agent’s preferences (e.g., “I don’t like Sam Moore” [Park, section 3.1.1 last paragraph]) are based on generated by a language model.)
and using the at least one second layer of the multi-layer memory including the at least one machine-learned preference to configure or control execution of the workflow by the automated agent. ([Park, figure 5]: The agent reflects, plans, and acts based on the retrieved memories [Park, figure 5]. Reflecting, planning, and acting each fall under the broadest reasonable interpretation of “configur[ing] or control[ling] execution of the workflow” as recited by the claim.)
Claim 2
Park discloses the elements of the parent claim(s). It also discloses:
[The method of claim 1, further comprising:] storing data obtained via an interaction between the entity and the automated agent in the first layer of the multi-layer memory; ([Park, section 3.4.2 and figure 5]: As noted under the parent claim, Park discloses that agents “remember their interactions with other agents” [Park, section 3.4.2] and that “the architecture retrieves relevant memories” from the memory stream [Park, figure 5 caption]. An agent’s interaction with another agent maps to “an interaction between the entity and the automated agent” as recited by the claim, the memory of this interaction to the “data” of the claim, and retrieving this memory from the memory stream maps to the step of “storing… in the first layer” as recited by the claim.)
and storing a machine-learned difference between the data obtained via the interaction and a machine-generated probable interaction between the entity and the automated agent in the second layer of the multi-layer memory. ([Park, section 4.3]: Park discloses agents continually making observations and choosing to “continue with their existing plan, or react” [Park, section 4.3.1]. If a reaction is appropriate, the system “regenerate[s] the agent’s existing plan from the time when the reaction takes place” [Park, section 4.3.1]. Park further indicates that plans re “save[d]… in the memory stream” [Park, section 4.3 paragraph beginning “The agent”]. The reaction suggested by the language model (e.g., “John could consider asking Eddy about his music composition project” [Park, section 4.3.1]) maps to the “machine-generated probable interaction” of the claim, the regenerated plan maps to the “machine-learned difference” of the claim, and storing this regenerated plan in the memory stream maps to the step of “storing [the] machine-learned difference… in the second layer” as recited by the claim.)
Claim 3
Park discloses the elements of the parent claim(s). It also discloses:
[The method of claim 1, further comprising:] storing data obtained via an interaction between the entity and the automated agent in the first layer of the multi-layer memory; ([Park, section 3.4.2 and figure 5]: As noted under the parent claim, Park discloses that agents “remember their interactions with other agents” [Park, section 3.4.2] and that “the architecture retrieves relevant memories” from the memory stream [Park, figure 5 caption]. An agent’s interaction with another agent maps to “an interaction between the entity and the automated agent” as recited by the claim, the memory of this interaction to the “data” of the claim, and retrieving this memory from the memory stream maps to the step of “storing… in the first layer” as recited by the claim.)
creating a compressed version of the data obtained via the interaction; and storing the compressed version of the data obtained via the interaction in the second layer of the multi-layer memory. ([Park, section 4.3]: Park discloses creating a plan by prompting a language model with a summary (including, e.g., a “summary of their recent experiences” and a “summary of their previous day”) and then “sav[ing] this plan in the memory stream” [Park, section 4.3 two paragraphs beginning “It would be”]. The summary of recent interactions maps to the “compression version of the data” of the claim and saving to the memory stream maps to “storing… in the second layer” as recited by the claim.)
Claim 4
Park discloses the elements of the parent claim(s). It also discloses:
[The method of claim 1, further comprising:] querying at least one of the first layer or the second layer for argument data; mapping the argument data to at least one argument of a prompt; applying a machine learning model to the prompt including the at least one argument to generate a plan; ([Park, section 4.3]: Park discloses “creat[ing] an initial plan” by “prompt[ing] the language model with the agent’s summary description (e.g., name, traits, and a summary of their recent experiences) and a summary of their previous day” [Park, section 4.3 paragraph beginning “It would be”]. Park gives a “full example prompt” which is “unfinished at the bottom for the language model to complete”, and the completion generated by the language model is a “rough sketch of the agent’s plan for a day” [Park, section 4.3 paragraph beginning “It would be”]. Any of the agent’s name, the agent’s traits, the summary of the agent’s recent experiences, or the summary of the agent’s previous day map to the “argument data” of the claim. The position in the prompt into which this data is inserted maps to the “at least one argument” of the claim, and the overall prompt maps to the “prompt” of the claim. The language model maps to the “machine learning model” of the claim, the plan it generates to the “plan” of the claim, and using it to generate the plan for the day maps to the step of “applying [the] machine learning model to the prompt… to generate [the] plan” as recited by the claim.)
and causing the automated agent to execute the plan. ([Park, figure 5]: The agents in Park act and execute their plans.)
Claim 5
Park discloses the elements of the parent claim(s). It also discloses:
[The method of claim 4, further comprising:] using a machine learning model, determining an order of precedence for querying the first layer and the second layer; and querying the first layer and the second layer in accordance with the order of precedence. ([Park, section 4.3 and appendix A]: As noted under the parent claim, Park discloses prompting a language model with a summary description of the agent (e.g., name and traits) and a summary of their previous day [Park, section 4.3 paragraph beginning “It would be”; see also, section 4.3.1 and appendix A]. Moreover, the information occurs in a particular linear order (in the depicted example, the name for example, occurs prior to the summary of the previous day). The order in which data is organized in this prompt is the “order of precedence” of the claim, and obtaining this information from the various memory components to generate the prompt maps to the step of “querying… in accordance with the order of precedence” recited by the claim. Since the language model is used to generate these prompts [Park, appendix A], these steps “us[e] a machine learning model” as recited by the claim.)
Claim 6
Park discloses the elements of the parent claim(s). It also discloses:
[The method of claim 1, further comprising:] assigning a first access level to the first layer; moving a subset of the context data to a third layer of the multi-layer memory; ([Park, figure 5]: As noted under the parent claim, Park discloses that “all perceptions are saved in a comprehensive record of the agent’s experiences called the memory stream” [Park, figure 5]. The perceptions map to the “subset of the context data” of the claim, the memory stream maps to the “third layer” of the claim, and the saving of perceptions maps to the step of “moving [the] subset of the context data to [the] third layer” as recited by the claim. The examiner notes that the broadest reasonable interpretation of the claim does not require the first, second, and third layers of memory to be distinct from one another, but the applicant is invited to consult SPark or Kim as cited in the conclusion of this Office action for references that may be used in combination for a third layer of memory that is distinct from the first and second layers.)
and assigning a second access level to the third layer, wherein the first access level is more restrictive than the second access level. ([Park, figure 5]: In the system disclosed by Park, the memory stream is more readily accessible for the perceptions than the retrieved memories since, as noted above, the perceptions are stored in the memory stream and may or may not then be retrieved for the purposes of planning, reflecting, and acting [Park, figure 5]. The accessibility for the purposes of saving perceptions maps to the “access levels” of the claim, and since perceptions are saved into the memory stream before potentially being retrieved for further use into the retrieved memories, the “first access level” of the retrieved memories (i.e., the “first layer” of the claim) is in fact “more restrictive” than the “second access level” of the memory stream (i.e., the “third layer” of the claim).)
Claim 7
Park discloses the elements of the parent claim(s). It also discloses:
[The method of claim 1, further comprising:] using the first layer of the multi-layer memory, machine-learning a definition or example of a term; and storing the machine-learned definition or example of the term in the second layer of the multi-layer memory. ([Park, sections 4.3 and 5.1]: Park gives an example where an agent, Eddy Lin, wants to “take a short walk around his workspace” [Park, section 5.1 paragraph beginning “To determine”]. The language model, when prompted to ask what area Eddy Lin should go to, responds “The Lin family’s house” [Park, section 5.1 paragraph beginning “To determine”]. The “area” that Eddy Lin should go to maps to the “term” of the claim, and output “[t]he Lin family’s house” generated by the language model maps to the “definition or example of [the] term” as recited by the claim. Since plan formulation makes use of retrieved memories, it “us[es] the first layer” as recited by the claim. Moreover, agents “saving [their] plans in the memory stream” [Park, section 4.3 paragraph beginning “The agent”] maps to the step of “storing… in the second layer” as recited by the claim.)
Claim 8
Park discloses the elements of the parent claim(s). It also discloses:
[The method of claim 1, further comprising:] using the first layer of the multi-layer memory, machine-learning a group or series of steps of the workflow; ([Park, section 4.3 and figure 5]: Park discloses “generating a rough sketch of the agent’s plan for a day” by prompting a language model with a summary (including, e.g., a “summary of their recent experiences” and a “summary of their previous day”) [Park, section 4.3 paragraph beginning “It would be”]. The plan generated by the language model maps to the “group or series of steps of the workflow” of the claim. Since formulating a plan uses the retrieved memories [Park, figure 5], it “us[es] the first layer” as required by the claim.)
and storing the machine-learned group or series of steps of the workflow in the second layer of the multi-layer memory. ([Park, section 4.3]: After the language model generates a plan, the “agent saves this plan in the memory stream” [Park, section 4.3 paragraph beginning “The agent”]. This maps to the step of “storing… in the second layer” as recited by the claim.)
Claim 9
Park discloses:
A system comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises at least one instruction that, when executed by the at least one processor, causes the at least one processor to be capable of performing at least one operation comprising: ([Park, section 1]: The system described in Park is implemented as computer code provided on GitHub [Park, section 1 footnote 2; see also, section 5]. This code maps to the “at least one instruction” of the claim. Any computer on which this code is executed maps to the “system” of the claim, with its processor and memory mapping to the “at least one processor” and “at least one memory” of the claim.)
determining an entity identity associated with an entity; using the entity identity, creating an automated agent comprising a multi-layer memory and a workflow; ([Park, sections 1, 3.1, and figure 5]: Park discusses “generative agents” based on large-language models that “create daily plans that reflect their characteristics and experiences, act out those plans, react, and re-plan when appropriate” [Park, section 1 paragraph beginning “generative agents”]. Each agent’s memory has two components: a “Memory Stream” and “Retrieved Memories” [Park, figure 5]. Park also discloses “author[ing] one paragraph of natural language description to depict each agent’s identity” and “[e]ach semicolon-delimited phrase [of the natural language description] is entered into the agent’s initial memory as memories at the start of the simulation” [Park, section 3.1 first paragraph]. Any of the agents maps to the “entity” of the claim. Its initial natural language description (or, alternatively, its memory stream, in which that natural language description is stored) maps to the “entity identity” of the claim. Any of the agents maps to the “automated agent” of the claim. Its multi-component memory structure maps to the “multi-layer memory” of the claim, and the agent’s plans and actions map to the “workflow” of the claim.)
storing context data in a first layer of the multi-layer memory, ([Park, figure 5]: As noted above, Park discloses each agent having two memory components: a memory stream and retrieved memories [Park, figure 5]. It discloses that “[a]gents perceive their environment, and all perceptions are saved in a comprehensive record of the agent’s experiences called the memory stream. Based on their perceptions, the architecture retrieves relevant memories” [Park, figure 5 caption]. The retrieved memories map to the “context data” and the “first layer” of the claim, and retrieving relevant memories maps to the step of “storing [the] context data in [the] first layer” as recited by the claim.)
wherein the context data is obtained using the entity identity; ([Park, section 3.1 and figure 5]: As noted above, the initial natural language description of each agent is “entered into the agent’s initial memory as memories at the start of the simulation” [Park, section 3.1 first paragraph]. Since the retrieved memories are retrieved from the memory stream, they are “obtained using the entity identity” as recited by the claim.)
storing at least one machine-learned entity preference in a second layer of the multi-layer memory, wherein the at least one machine-learned entity preference is machine-learned using the context data; ([Park, section 3 and figure 5]: As noted above, the initial natural language description of each agent is “entered into the agent’s initial memory as memories at the start of the simulation” [Park, section 3.1 first paragraph]. The initial natural language description includes the agent’s preferences (e.g., “John Lin… loves to help people. He is always looking for ways to make the process of getting medication easier for his customers… John Lin loves his family very much… John Lin thinks Sam Moore is a nice and kind man… John Lin and Tom Moreno are friends and like to discuss local politics together” [Park, section 3.1 first paragraph]). Moreover, as agent behavior is simulated, they “remember their interactions with other agents” [Park, section 3.4.2] and new preferences are added to the memory stream based on these actions (e.g., “I don’t like Sam Moore” [Park, section 3.1.1 last paragraph] or “We have all agreed to vote for him because we like his platform” [Park, section 3.1.2 first paragraph]). The memory stream maps to the “second layer” of the claim, and the agent’s preferences stored in the stream map to the “at least one machine-learned entity preference” of the claim. (The examiner notes tangentially that the claim as presently recited does not require the “first layer” and the “second layer” to be distinct from one another, so, in an alternative mapping of claim elements, the memory stream and the retrieved memories taken together could be mapped to the “first layer” of the claim as well as the “second layer” of the claim.) While the phrase “machine-learned” is indefinite as recited (cf. 112(b) rejections), at some of the agent’s preferences (e.g., “I don’t like Sam Moore” [Park, section 3.1.1 last paragraph]) are based on generated by a language model.)
and using the at least one second layer of the multi-layer memory including the at least one machine-learned preference to configure or control execution of the workflow by the automated agent. ([Park, figure 5]: The agent reflects, plans, and acts based on the retrieved memories [Park, figure 5]. Reflecting, planning, and acting each fall under the broadest reasonable interpretation of “configur[ing] or control[ling] execution of the workflow” as recited by the claim.)
Claims 10-16 inherit limitations from claim 9 and recite additional limitations which are substantially similar to those recited by claims 2-8, respectively, so they are rejected by the same rationale.
Claim 17
Park discloses:
At least one non-transitory machine-readable storage medium comprising at least one instruction that, when executed by at least one processor, causes the at least one processor to: ([Park, section 1]: The system described in Park is implemented as computer code provided on GitHub [Park, section 1 footnote 2; see also, section 5]. This code maps to the “at least one instruction” of the claim. The hard drive of any computer on which this code is executed, or, alternatively, the GitHub servers on which this code is stored maps to the “at least one non-transitory machine-readable storage medium” of the claim.)
determine an entity identity associated with an entity; using the entity identity, create an automated agent comprising a multi-layer memory and a workflow; ([Park, sections 1, 3.1, and figure 5]: Park discusses “generative agents” based on large-language models that “create daily plans that reflect their characteristics and experiences, act out those plans, react, and re-plan when appropriate” [Park, section 1 paragraph beginning “generative agents”]. Each agent’s memory has two components: a “Memory Stream” and “Retrieved Memories” [Park, figure 5]. Park also discloses “author[ing] one paragraph of natural language description to depict each agent’s identity” and “[e]ach semicolon-delimited phrase [of the natural language description] is entered into the agent’s initial memory as memories at the start of the simulation” [Park, section 3.1 first paragraph]. Any of the agents maps to the “entity” of the claim. Its initial natural language description (or, alternatively, its memory stream, in which that natural language description is stored) maps to the “entity identity” of the claim. Any of the agents maps to the “automated agent” of the claim. Its multi-component memory structure maps to the “multi-layer memory” of the claim, and the agent’s plans and actions map to the “workflow” of the claim.)
store context data in a first layer of the multi-layer memory, ([Park, figure 5]: As noted above, Park discloses each agent having two memory components: a memory stream and retrieved memories [Park, figure 5]. It discloses that “[a]gents perceive their environment, and all perceptions are saved in a comprehensive record of the agent’s experiences called the memory stream. Based on their perceptions, the architecture retrieves relevant memories” [Park, figure 5 caption]. The retrieved memories map to the “context data” and the “first layer” of the claim, and retrieving relevant memories maps to the step of “storing [the] context data in [the] first layer” as recited by the claim.)
wherein the context data is obtained using the entity identity; ([Park, section 3.1 and figure 5]: As noted above, the initial natural language description of each agent is “entered into the agent’s initial memory as memories at the start of the simulation” [Park, section 3.1 first paragraph]. Since the retrieved memories are retrieved from the memory stream, they are “obtained using the entity identity” as recited by the claim.)
store at least one machine-learned entity preference in a second layer of the multi-layer memory, wherein the at least one machine-learned entity preference is machine-learned using the context data; ([Park, section 3 and figure 5]: As noted above, the initial natural language description of each agent is “entered into the agent’s initial memory as memories at the start of the simulation” [Park, section 3.1 first paragraph]. The initial natural language description includes the agent’s preferences (e.g., “John Lin… loves to help people. He is always looking for ways to make the process of getting medication easier for his customers… John Lin loves his family very much… John Lin thinks Sam Moore is a nice and kind man… John Lin and Tom Moreno are friends and like to discuss local politics together” [Park, section 3.1 first paragraph]). Moreover, as agent behavior is simulated, they “remember their interactions with other agents” [Park, section 3.4.2] and new preferences are added to the memory stream based on these actions (e.g., “I don’t like Sam Moore” [Park, section 3.1.1 last paragraph] or “We have all agreed to vote for him because we like his platform” [Park, section 3.1.2 first paragraph]). The memory stream maps to the “second layer” of the claim, and the agent’s preferences stored in the stream map to the “at least one machine-learned entity preference” of the claim. (The examiner notes tangentially that the claim as presently recited does not require the “first layer” and the “second layer” to be distinct from one another, so, in an alternative mapping of claim elements, the memory stream and the retrieved memories taken together could be mapped to the “first layer” of the claim as well as the “second layer” of the claim.) While the phrase “machine-learned” is indefinite as recited (cf. 112(b) rejections), at some of the agent’s preferences (e.g., “I don’t like Sam Moore” [Park, section 3.1.1 last paragraph]) are based on generated by a language model.)
and use the at least one second layer of the multi-layer memory including the at least one machine-learned preference to configure or control execution of the workflow by the automated agent. ([Park, figure 5]: The agent reflects, plans, and acts based on the retrieved memories [Park, figure 5]. Reflecting, planning, and acting each fall under the broadest reasonable interpretation of “configur[ing] or control[ling] execution of the workflow” as recited by the claim.)
Claims 18-20 inherit limitations from claim 17 and recite additional limitations which are substantially similar to those recited by claims 2-4, respectively, so they are rejected by the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lei WANG et al. (A Survey on Large Language Model based Autonomous Agents, published 2024-04-24; hereafter, “Wang”) discusses LLM-based autonomous agents [Wang, title] including “Hybrid Memory” structures in which “short-term memory temporarily buffers recent perceptions, while long-term memory consolidates important information over time” [Wang, section 2.1.2]. It also cites numerous other prior art references which describe agents having such a memory structure.
Gail CARPENTER et al. (US5214715, published 1993-05-25; hereafter, “Carpenter”) discusses autonomous agents [Carpenter, column 5 lines 29-42] using an architecture that has “short term memory (STM) fields [that] hold new patterns relative to each input pattern. The long term memory (LTM), on the other hand, defines patterns learned from some number of input patterns, that is, over a relatively longer period of time” [Carpenter, column 1 lines 47-51].
Sanjun PARK et al. (Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture, published 2024-02-03; hereafter, “SPark”) discloses a memory structure having working memory, short-term memory, and long-term memory [SPark, figure 1].
Taewoon KIM et al. (A Machine with Short-Term, Episodic, and Semantic Memory Systems, published 2023; hereafter, “Kim”) describes “an agent with short-term, episodic, and semantic memory systems” [Kim, abstract].
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/S.A./Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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